| Copyright | (c) Adam Scibior 2015-2020 |
|---|---|
| License | MIT |
| Maintainer | leonhard.markert@tweag.io |
| Stability | experimental |
| Portability | GHC |
| Safe Haskell | Safe-Inferred |
| Language | Haskell2010 |
Control.Monad.Bayes.Inference.SMC
Description
Sequential Monte Carlo (SMC) sampling.
Arnaud Doucet and Adam M. Johansen. 2011. A tutorial on particle filtering and smoothing: fifteen years later. In The Oxford Handbook of Nonlinear Filtering, Dan Crisan and Boris Rozovskii (Eds.). Oxford University Press, Chapter 8.
Synopsis
- smc :: MonadDistribution m => SMCConfig m -> SequentialT (PopulationT m) a -> PopulationT m a
- smcPush :: MonadMeasure m => SMCConfig m -> SequentialT (PopulationT m) a -> PopulationT m a
- data SMCConfig m = SMCConfig {
- resampler :: forall x. PopulationT m x -> PopulationT m x
- numSteps :: Int
- numParticles :: Int
Documentation
smc :: MonadDistribution m => SMCConfig m -> SequentialT (PopulationT m) a -> PopulationT m a Source #
Sequential importance resampling. Basically an SMC template that takes a custom resampler.
smcPush :: MonadMeasure m => SMCConfig m -> SequentialT (PopulationT m) a -> PopulationT m a Source #
Sequential Monte Carlo with multinomial resampling at each timestep. Weights are normalized at each timestep and the total weight is pushed as a score into the transformed monad.
Constructors
| SMCConfig | |
Fields
| |